Anaerobic CUE
05 Differential abundance modelling
Roey Angel
2021-02-10
Differential abundance modelling of SIP gradients
Here we attempt to detect ASVs that were labelled with 13C in our soil incubations using differential abundance modelling. Using DESeq2 (Love, Huber and Anders 2014) we compare the relative abundance of each ASV in the fractions where 13C-labelled RNA is expected to be found (>1.795 g ml-1; AKA ‘heavy’ fractions) to the fractions where unlabelled RNA is expected to be found (<1.795 g ml-1; AKA ‘light’ fractions). The method has been previously described in Angel et al., (2018).
Setting general parameters:
set.seed(2021)
alpha_thresh <- 0.05
LFC_thresh <- 0.26
samples_prep_path <- "./"
data_path <- "./DADA2_pseudo/"
# Metadata_table <- "./AnCUE_Metadata_decontam.csv"
# Seq_table <- "DADA2.seqtab_nochim_decontam.tsv"
# Seq_file <- "DADA2.Seqs_decontam.fa"
Ps_file <- "Ps_obj_decontam_filt3.Rds"
Tree_file <- "./Tree/DADA2.Seqs_decontam_filtered.filtered.align.treefile"Read phyloseq object
Subset the dataset
Because the DESeq2 models will be run on each gradient separately, we need to subset This is easily done using HTSSIP::phyloseq_subset (Youngblut, Barnett and Buckley 2018)
# split, ignore time points (for labelled ASV plots)
test_expr_1 <- "(Site == '${Site}' & Oxygen == '${Oxygen}' & Label..13C. == 'Unlabelled') | (Site == '${Site}' & Oxygen == '${Oxygen}' & Label..13C. == '${Label..13C.}')"
params_1 <- get_treatment_params(Ps_obj_SIP, c("Site",
"Oxygen",
"Glucose",
"Label..13C."),
"Label..13C. != 'Unlabelled'")
Ps_obj_SIP_noTime_l <- phyloseq_subset(Ps_obj_SIP, params_1, test_expr_1)
# names(Ps_obj_SIP_noTime_l) %<>%
# map(., ~str_remove_all(.x, "\\s\\|\\s.*")) %>%
# map(., ~str_remove_all(.x, "\\(|\\)|Site == |Hours == |Oxygen == |Label..13C. == |'")) %>%
# map(., ~str_replace_all(.x, "([0-9]+)", "\\1 h"))
# split, include time points (for DESeq2 modelling)
test_expr_2 <- "(Site == '${Site}' & Hours == '${Hours}' & Oxygen == '${Oxygen}' & Label..13C. == '${Label..13C.}') | (Site == '${Site}' & Hours == '${Hours}' & Oxygen == '${Oxygen}' & Label..13C. == '${Label..13C.}')"
params_2 <- get_treatment_params(Ps_obj_SIP, c("Site",
"Hours",
"Oxygen",
"Glucose",
"Label..13C."))
# Generate a list of subsetted phyloseq objects
Ps_obj_SIP_byTime_l <- phyloseq_subset(Ps_obj_SIP, params_2, test_expr_2)
names(Ps_obj_SIP_byTime_l) %<>%
map(., ~str_remove_all(.x, "\\s\\|\\s.*")) %>%
map(., ~str_remove_all(.x, "\\(|\\)|Site == |Hours == |Oxygen == |Label..13C. == |'")) %>%
map(., ~str_replace_all(.x, "([0-9]+)", "\\1 h")) Beta diversity analysis
Let us look first at the dissimilarity in community composition between the different fractions. If the labelling was strong enough we should see a deivation of (some of) the heavy fractions from the light ones. However, a lack of a significant deviation does not mean unsuccesful labelling because if only a small minority of the community was labelled we might not see it here (but we will, hopefully, see it using DESeq2 modelling).
(mod1 <- adonis(vegdist(otu_table(Ps_obj_SIP), method = "horn") ~ Site * Oxygen * Hours + Lib.size,
data = as(sample_data(Ps_obj_SIP), "data.frame"),
permutations = 999
))##
## Call:
## adonis(formula = vegdist(otu_table(Ps_obj_SIP), method = "horn") ~ Site * Oxygen * Hours + Lib.size, data = as(sample_data(Ps_obj_SIP), "data.frame"), permutations = 999)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Site 1 27.443 27.4431 480.48 0.53921 0.001 ***
## Oxygen 1 5.290 5.2896 92.61 0.10393 0.001 ***
## Hours 1 0.160 0.1596 2.80 0.00314 0.054 .
## Lib.size 1 0.078 0.0777 1.36 0.00153 0.244
## Site:Oxygen 1 0.540 0.5404 9.46 0.01062 0.001 ***
## Site:Hours 1 0.419 0.4191 7.34 0.00823 0.003 **
## Oxygen:Hours 1 0.119 0.1185 2.08 0.00233 0.116
## Site:Oxygen:Hours 1 0.226 0.2259 3.96 0.00444 0.021 *
## Residuals 291 16.621 0.0571 0.32657
## Total 299 50.895 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Ps_obj_SIP %>%
scale_libraries(round = "round") ->
Ps_obj_SIP_scaled
plot_lib_dist(Ps_obj_SIP_scaled)
(mod2 <- adonis(vegdist(otu_table(Ps_obj_SIP_scaled), method = "horn") ~ Site * Oxygen * Hours + Lib.size,
data = as(sample_data(Ps_obj_SIP_scaled), "data.frame"),
permutations = 999
))##
## Call:
## adonis(formula = vegdist(otu_table(Ps_obj_SIP_scaled), method = "horn") ~ Site * Oxygen * Hours + Lib.size, data = as(sample_data(Ps_obj_SIP_scaled), "data.frame"), permutations = 999)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Site 1 27.658 27.6580 482.57 0.53919 0.001 ***
## Oxygen 1 5.381 5.3811 93.89 0.10490 0.001 ***
## Hours 1 0.161 0.1611 2.81 0.00314 0.054 .
## Lib.size 1 0.069 0.0691 1.21 0.00135 0.304
## Site:Oxygen 1 0.582 0.5817 10.15 0.01134 0.001 ***
## Site:Hours 1 0.426 0.4265 7.44 0.00831 0.001 ***
## Oxygen:Hours 1 0.112 0.1115 1.95 0.00217 0.139
## Site:Oxygen:Hours 1 0.228 0.2279 3.98 0.00444 0.021 *
## Residuals 291 16.678 0.0573 0.32514
## Total 299 51.295 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(mod3 <- adonis(vegdist(otu_table(Ps_obj_SIP_scaled), method = "horn") ~ Site * Oxygen * Hours * Density.zone,
data = as(sample_data(Ps_obj_SIP_scaled), "data.frame"),
permutations = 999
))##
## Call:
## adonis(formula = vegdist(otu_table(Ps_obj_SIP_scaled), method = "horn") ~ Site * Oxygen * Hours * Density.zone, data = as(sample_data(Ps_obj_SIP_scaled), "data.frame"), permutations = 999)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Site 1 27.658 27.6580 717.77 0.53919 0.001 ***
## Oxygen 1 5.381 5.3811 139.65 0.10490 0.001 ***
## Hours 1 0.161 0.1611 4.18 0.00314 0.025 *
## Density.zone 1 2.449 2.4491 63.56 0.04774 0.001 ***
## Site:Oxygen 1 0.513 0.5133 13.32 0.01001 0.001 ***
## Site:Hours 1 0.405 0.4054 10.52 0.00790 0.001 ***
## Oxygen:Hours 1 0.078 0.0778 2.02 0.00152 0.139
## Site:Density.zone 1 0.189 0.1885 4.89 0.00367 0.006 **
## Oxygen:Density.zone 1 2.813 2.8134 73.01 0.05485 0.001 ***
## Hours:Density.zone 1 0.032 0.0320 0.83 0.00062 0.463
## Site:Oxygen:Hours 1 0.174 0.1735 4.50 0.00338 0.012 *
## Site:Oxygen:Density.zone 1 0.065 0.0654 1.70 0.00128 0.190
## Site:Hours:Density.zone 1 0.109 0.1088 2.82 0.00212 0.057 .
## Oxygen:Hours:Density.zone 1 0.229 0.2294 5.95 0.00447 0.002 **
## Site:Oxygen:Hours:Density.zone 1 0.095 0.0946 2.46 0.00184 0.098 .
## Residuals 284 10.943 0.0385 0.21334
## Total 299 51.295 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Site_disp <- betadisper(vegdist(otu_table(Ps_obj_SIP_scaled), method = "horn"), get_variable(Ps_obj_SIP_scaled, "Site"))
permutest(Site_disp)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.2375 0.237546 5.5678 999 0.016 *
## Residuals 298 12.7139 0.042664
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Oxygen_disp <- betadisper(vegdist(otu_table(Ps_obj_SIP_scaled), method = "horn"), get_variable(Ps_obj_SIP_scaled, "Oxygen"))
permutest(Oxygen_disp)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.03023 0.0302278 3.1681 999 0.087 .
## Residuals 298 2.84331 0.0095413
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Hours_disp <- betadisper(vegdist(otu_table(Ps_obj_SIP_scaled), method = "horn"), get_variable(Ps_obj_SIP_scaled, "Hours"))
permutest(Hours_disp)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 4 0.2595 0.064877 4.7232 999 0.002 **
## Residuals 295 4.0520 0.013736
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Density_disp <- betadisper(vegdist(otu_table(Ps_obj_SIP_scaled), method = "horn"), get_variable(Ps_obj_SIP_scaled, "Density.zone"))
permutest(Density_disp)##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.3893 0.38928 35.36 999 0.001 ***
## Residuals 298 3.2806 0.01101
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Ord <- ordinate(Ps_obj_SIP_scaled, "CAP", "horn",
formula = ~ Site * Oxygen * Hours * Density.zone)
explained <- as.numeric(format(round(eigenvals(Ord)/sum(eigenvals(Ord)) * 100, 1), nsmall = 1))
Ord_plt <- plot_ordination(Ps_obj_SIP, Ord, type = "samples", color = "Label..13C.", justDF = TRUE)
p_ord_joint <- ggplot(Ord_plt) +
geom_point(aes(
x = CAP1,
y = CAP2,
color = Label..13C.,
size = Density..g.ml.1.,
shape = Oxygen
), alpha = 2 / 3) +
guides(colour = guide_legend(title = "Labelling"),
size = guide_legend(title = "Density (g ml<sup>-1</sup>)"),
shape = guide_legend(title = "Oxygen")) +
scale_colour_locuszoom() +
# scale_colour_manual(values = Gradient.colours) +
# scale_fill_manual(values = Gradient.colours, guide = "none") +
labs(x = sprintf("CAP1 (%s%%)", explained[1]),
y = sprintf("CAP2 (%s%%)", explained[2])) +
coord_fixed(ratio = sqrt(explained[2] / explained[1])) +
theme(legend.justification = "top",
legend.title = element_markdown(size = 11)
) +
scale_size_continuous(breaks = round(c(seq(min(Ord_plt$Density..g.ml.1.),
max(Ord_plt$Density..g.ml.1.),
length.out = 5),
1), 4),
range = c(0.1, 5)) +
facet_grid(Site ~ Hours) +
# ggtitle("Joint analysis") +
NULL
save_figure(paste0(fig.path, "Oridnation"),
p_ord_joint,
pwidth = 10,
pheight = 8,
dpi = 600)
knitr::include_graphics(paste0(fig.path, "Oridnation", ".png"))
Differential abundance models
Now run the differential abundance models using DESeq2. We then filter the resutls to include only ASVs with Log_2_ fold change >LFC_thresh and significant at P<alpha_thresh. Lastly, we run ‘LFC-shrinking’ based on Stephens (2016).
# generate a deseq object
DESeq_obj_SIP_byTime_l <- mclapply(Ps_obj_SIP_byTime_l,
function(x) {phyloseq_to_deseq2_safe(x,
test_condition = "Density.zone",
ref_level = "Light")},
mc.cores = nrow(params_2))
# run dds pipeline
DESeq_obj_SIP_byTime_l %<>% mclapply(.,
function(x) {DESeq(x,
test = "Wald",
fitType = "local")},
mc.cores = nrow(params_2)) # run dds pipeline
# extract results from a DESeq analysis
DESeq_res_SIP_byTime_l <- mclapply(DESeq_obj_SIP_byTime_l,
function(x) {
results(x,
altHypothesis = "greater",
alpha = alpha_thresh,
contrast = c("Density.zone", "Heavy", "Light"))}, # redundant if phyloseq_to_deseq2_safe() was used but doesn't hurt
mc.cores = nrow(params_2))
DESeq_res_SIP_byTime_LFC_l <- mclapply(DESeq_obj_SIP_byTime_l,
function(x) {
results(x,
lfcThreshold = LFC_thresh,
altHypothesis = "greater",
alpha = alpha_thresh,
contrast = c("Density.zone", "Heavy", "Light"))}, # redundant if phyloseq_to_deseq2_safe() was used but doesn't hurt
mc.cores = nrow(params_2)) # Extract results from a DESeq analysis
DESeq_res_SIP_byTime_LFC_shrink_l <- map(seq(length(DESeq_obj_SIP_byTime_l)),
~lfcShrink(DESeq_obj_SIP_byTime_l[[.x]],
res = DESeq_res_SIP_byTime_LFC_l[[.x]],
coef = "Density.zone_Heavy_vs_Light",
type = "ashr"))
names(DESeq_res_SIP_byTime_LFC_shrink_l) <- names(DESeq_res_SIP_byTime_LFC_l)
# Compare
plotMA(DESeq_res_SIP_byTime_l[[2]], ylim = c(-2,2))
# summarise results (lfcShrink doesn't change the values)
# map2(DESeq_res_SIP_byTime_l, print(names(DESeq_res_SIP_byTime_l)), ~summary(.x)) # summarise results
# for (i in seq(1, length(DESeq_res_SIP_byTime_l))) { # didn't manage with map
# print(names(DESeq_res_SIP_byTime_l[i]))
# summary(DESeq_res_SIP_byTime_l[[i]])
# }
for (i in seq(1, length(DESeq_res_SIP_byTime_LFC_l))) { # didn't manage with map
print(names(DESeq_res_SIP_byTime_LFC_l[i]))
summary(DESeq_res_SIP_byTime_LFC_l[[i]])
}## [1] "Certovo & 12 h & Anoxic & Labelled"
##
## out of 3026 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 3, 0.099%
## low counts [2] : 7, 0.23%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 216 h & Anoxic & Labelled"
##
## out of 2878 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 37, 1.3%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1484, 52%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 24 h & Anoxic & Labelled"
##
## out of 2755 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 1, 0.036%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 4, 0.15%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 48 h & Anoxic & Labelled"
##
## out of 2787 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 15, 0.54%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1222, 44%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 216 h & Anoxic & Unlabelled"
##
## out of 2825 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 5, 0.18%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 12 h & Oxic & Labelled"
##
## out of 3053 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 7, 0.23%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 8, 0.26%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 24 h & Oxic & Labelled"
##
## out of 2954 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 107, 3.6%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 905, 31%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 48 h & Oxic & Labelled"
##
## out of 2882 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 122, 4.2%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 331, 11%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 72 h & Oxic & Labelled"
##
## out of 2898 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 142, 4.9%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 1, 0.035%
## low counts [2] : 1329, 46%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Certovo & 72 h & Oxic & Unlabelled"
##
## out of 2979 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 6, 0.2%
## low counts [2] : 0, 0%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 12 h & Anoxic & Labelled"
##
## out of 3031 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 27, 0.89%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1678, 55%
## (mean count < 3)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 216 h & Anoxic & Labelled"
##
## out of 3210 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 116, 3.6%
## low counts [2] : 3, 0.093%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 24 h & Anoxic & Labelled"
##
## out of 2848 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 22, 0.77%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 2340, 82%
## (mean count < 15)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 48 h & Anoxic & Labelled"
##
## out of 2758 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 12, 0.44%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1527, 55%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 216 h & Anoxic & Unlabelled"
##
## out of 2862 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 2, 0.07%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 12 h & Oxic & Labelled"
##
## out of 2923 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 57, 2%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 1, 0.034%
## low counts [2] : 1790, 61%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 24 h & Oxic & Labelled"
##
## out of 2548 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 101, 4%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1061, 42%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 48 h & Oxic & Labelled"
##
## out of 2531 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 13, 0.51%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1820, 72%
## (mean count < 3)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 72 h & Oxic & Labelled"
##
## out of 2556 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 115, 4.5%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 0, 0%
## low counts [2] : 1113, 44%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
##
## [1] "Plesne & 72 h & Oxic & Unlabelled"
##
## out of 2798 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up) : 3, 0.11%
## LFC < -0.26 (down) : 0, 0%
## outliers [1] : 7, 0.25%
## low counts [2] : 0, 0%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results# Store labelled OTUs and save them to a file
# DESeq_res_SIP_byTime_l %>%
# map(., ~subset(.x, padj < alpha_thresh & log2FoldChange > LFC_thresh)) %>%
# map(., ~as.data.frame(.x)) %>%
# map(., ~rownames_to_column(.x, "ASV")) %>%
# bind_rows(., .id = "Comparison") %>%
# arrange(Comparison, desc(baseMean)) %T>%
# write_csv(., file = "DESeq2_byTime_a-0.05.txt") ->
# DESeq_res_SIP_byTime_df
DESeq_res_SIP_byTime_LFC_shrink_l %>%
map(., ~subset(.x, padj < alpha_thresh & log2FoldChange > LFC_thresh)) %>%
map(., ~as.data.frame(.x)) %>%
map(., ~rownames_to_column(.x, "ASV")) %>%
bind_rows(., .id = "Comparison") %>%
arrange(Comparison, desc(baseMean)) %>%
separate(., "Comparison" ,c("Site","Hours", "Oxygen", "Label"), sep = " & ") %T>%
write_csv(., file = "DESeq2_byTime_a-0.05_LFC0-322.txt") ->
DESeq_res_SIP_byTime_LFC_sig_dfInspect results
Plot differential abundance models
# ps_obj <- Ps_obj_SIP
# DESeq_results <- DESeq_res_SIP_byTime_LFC0.322_l[9]
# plot_DESeq(DESeq_results, ps_obj, plot_title = names(DESeq_results))
DESeq_plots <- map(seq(length(DESeq_res_SIP_byTime_LFC_shrink_l)),
~plot_DESeq(DESeq_res_SIP_byTime_LFC_shrink_l[.x],
Ps_obj_SIP, plot_title = names(DESeq_res_SIP_byTime_LFC_shrink_l[.x])))
Certovo_DESeq <- ((DESeq_plots[[6]] +
theme(legend.position = "none") +
theme(axis.text.x = element_blank())) +
(DESeq_plots[[1]] +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())) +
(DESeq_plots[[7]] +
theme(legend.position = "none",
axis.text.x = element_blank())) +
(DESeq_plots[[3]] +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())) +
(DESeq_plots[[8]] +
theme(legend.position = "none") +
theme(legend.position = "none",
axis.text.x = element_blank())) +
(DESeq_plots[[4]] +
theme(legend.position = "none") +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank()) +
ylim(NA, 5)) +
(DESeq_plots[[9]] +
theme(legend.position = "none",
axis.text.x = element_blank())) +
(DESeq_plots[[2]] +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())) +
(DESeq_plots[[10]] +
theme(legend.position = "none")) +
(DESeq_plots[[5]] +
theme(legend.position = "none",
axis.title.y = element_blank())) +
plot_layout(ncol = 2, guides = "collect") &
theme(legend.position = 'bottom'))
save_figure(paste0(fig.path, "Certovo_DESeq2"),
Certovo_DESeq,
pwidth = 14,
pheight = 12,
dpi = 600)
knitr::include_graphics(paste0(fig.path, "Certovo_DESeq2", ".png"))
Plesne_DESeq <- ((DESeq_plots[[16]] +
theme(legend.position = "none") +
theme(axis.text.x = element_blank())) +
(DESeq_plots[[11]] +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())) +
(DESeq_plots[[17]] +
theme(legend.position = "none",
axis.text.x = element_blank())) +
(DESeq_plots[[13]] +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())) +
(DESeq_plots[[18]] +
theme(legend.position = "none") +
theme(legend.position = "none",
axis.text.x = element_blank())) +
(DESeq_plots[[14]] +
theme(legend.position = "none") +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank())) +
(DESeq_plots[[19]] +
theme(legend.position = "none",
axis.text.x = element_blank())) +
(DESeq_plots[[12]] +
theme(legend.position = "none",
axis.text.x = element_blank(),
axis.title.y = element_blank()) +
ylim(-3, NA)) +
(DESeq_plots[[20]] +
theme(legend.position = "none")) +
(DESeq_plots[[15]] +
theme(legend.position = "none",
axis.title.y = element_blank())) +
plot_layout(ncol = 2, guides = "collect") &
theme(legend.position = 'bottom'))
save_figure(paste0(fig.path, "Plesne_DESeq2"),
Plesne_DESeq,
pwidth = 14,
pheight = 12,
dpi = 600)
knitr::include_graphics(paste0(fig.path, "Plesne_DESeq2", ".png"))
Plot labelled ASVs
plot_combintions <- crossing(Site = c("Certovo", "Plesne"),
Oxygen = c("Oxic", "Anoxic"))
Labelled_ASVs <- map(seq(length(Ps_obj_SIP_noTime_l)), ~plot_otus_by_density(Ps_obj_SIP_noTime_l[[.x]],
ASV2plot = filter(DESeq_res_SIP_byTime_LFC_sig_df, Site == plot_combintions$Site[.x], Oxygen == plot_combintions$Oxygen[.x])))
map(seq(length(Ps_obj_SIP_noTime_l)),
~save_figure(paste0(fig.path, "Labelled_ASVs_", paste(plot_combintions[.x, ], collapse = "_")),
Labelled_ASVs[[.x]],
pwidth = 16,
pheight = 12,
dpi = 600))## [[1]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Certovo_Anoxic.svgz"
##
## [[2]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Certovo_Oxic.svgz"
##
## [[3]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Plesne_Anoxic.svgz"
##
## [[4]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Plesne_Oxic.svgz"## [[1]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Certovo_Anoxic.png"
## attr(,"class")
## [1] "knit_image_paths" "knit_asis"
##
## [[2]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Certovo_Oxic.png"
## attr(,"class")
## [1] "knit_image_paths" "knit_asis"
##
## [[3]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Plesne_Anoxic.png"
## attr(,"class")
## [1] "knit_image_paths" "knit_asis"
##
## [[4]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Plesne_Oxic.png"
## attr(,"class")
## [1] "knit_image_paths" "knit_asis"Plot phylogenetic trees with heatmaps
c("Certovo Oxic 12 h", "Certovo Oxic 24 h", "Certovo Oxic 48 h", "Certovo Oxic 72 h", "Certovo Anoxic 12 h", "Certovo Anoxic 24 h", "Certovo Anoxic 48 h", "Certovo Anoxic 216 h", "Plesne Oxic 12 h", "Plesne Oxic 24 h", "Plesne Oxic 48 h", "Plesne Oxic 72 h", "Plesne Anoxic 12 h", "Plesne Anoxic 24 h", "Plesne Anoxic 48 h", "Plesne Anoxic 216 h" ) ->
col_order
DESeq_res_SIP_byTime_LFC_l %>%
map(., ~as.data.frame(.x)) %>%
map(., ~rownames_to_column(.x, "ASV")) %>%
bind_rows(., .id = "Comparison") %>%
filter(str_detect(Comparison, "Labelled")) %>% # remove unlabelled samples [c(-5, -10, -15, -20)]
mutate(Labelled = ifelse(padj < alpha_thresh & log2FoldChange > LFC_thresh, "Labelled", "Unlabelled")) %>%
# arrange(Comparison, desc(baseMean)) %>%
separate(., "Comparison" ,c("Site","Hours", "Oxygen", "Label"), sep = " & ") %>%
mutate(Site_Oxygen_Hours = paste(Site, Oxygen, Hours)) %>%
mutate(across(Site_Oxygen_Hours, ~factor(., levels = col_order))) ->
# mutate(Site_oxygen = paste(Site, Oxygen)) ->
DESeq_res_SIP_byTime_all_df
# Summarise number of labelled and unlabelled ASVs
DESeq_res_SIP_byTime_all_df %>%
group_by(Labelled) %>%
summarise(n = n()) | Labelled | n |
|---|---|
| Labelled | 778 |
| Unlabelled | 37162 |
| NA | 21148 |
# detect taxa with NA from DESeq analysis
DESeq_res_SIP_byTime_all_df %<>%
filter(!is.na(Labelled)) #%>%
# pull(Labelled) ->
# bad_seqs
# remove NA taxa from PS obj
Ps_obj_SIP %>%
prune_taxa(setdiff(taxa_names(Ps_obj_SIP), "Seq_2375"), .) %>% # outlier
prune_taxa(DESeq_res_SIP_byTime_all_df$ASV, .) ->
Ps_obj_SIP4tree_plot
taxa2plot <- tibble(rank = c(rep("Class", 3), rep("Phylum", 4)),
subrank = c(rep("Order", 3), rep("Class", 4)),
Taxa2plot = c("Actinobacteria",
"Alphaproteobacteria",
"Gammaproteobacteria",
"Acidobacteriota",
"Verrucomicrobiota",
"Bacteroidota",
"Firmicutes"),
pwidth = c(12, 14, 16, 8, 8, 8, 8),
pheight = c(rep(10, 7)))
map(seq(nrow(taxa2plot)),
~wrap_ggtree_heatmap(Ps_obj_SIP4tree_plot,
DESeq_res_SIP_byTime_all_df,
rank = taxa2plot$rank[.x],
subrank = taxa2plot$subrank[.x],
Taxa2plot = taxa2plot$Taxa2plot[.x]))## [[1]]
## [1] "05_Diff_abund_figures/Tree_HM_Actinobacteria.svgz"
##
## [[2]]
## [1] "05_Diff_abund_figures/Tree_HM_Alphaproteobacteria.svgz"
##
## [[3]]
## [1] "05_Diff_abund_figures/Tree_HM_Gammaproteobacteria.svgz"
##
## [[4]]
## [1] "05_Diff_abund_figures/Tree_HM_Acidobacteriota.svgz"
##
## [[5]]
## [1] "05_Diff_abund_figures/Tree_HM_Verrucomicrobiota.svgz"
##
## [[6]]
## [1] "05_Diff_abund_figures/Tree_HM_Bacteroidota.svgz"
##
## [[7]]
## [1] "05_Diff_abund_figures/Tree_HM_Firmicutes.svgz"trees2display <- list.files(path = paste0(fig.path),
pattern = "^Tree_HM_(.*).png$",
full.names = TRUE)
knitr::include_graphics(trees2display)
Current session info
─ Session info ─────────────────────────────────────────────────────────────────────────
setting value
version R version 4.0.3 (2020-10-10)
os Ubuntu 18.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Prague
date 2021-02-10
─ Packages ─────────────────────────────────────────────────────────────────────────────
package * version date lib
ade4 1.7-16 2020-10-28 [1]
annotate 1.66.0 2020-04-27 [1]
AnnotationDbi 1.50.3 2020-07-25 [1]
ape 5.4-1 2020-08-13 [1]
aplot 0.0.6 2020-09-03 [1]
ashr 2.2-47 2020-02-20 [1]
assertthat 0.2.1 2019-03-21 [1]
backports 1.2.1 2020-12-09 [1]
Biobase * 2.48.0 2020-04-27 [1]
BiocGenerics * 0.34.0 2020-04-27 [1]
BiocManager 1.30.10 2019-11-16 [1]
BiocParallel 1.22.0 2020-04-27 [1]
biomformat 1.16.0 2020-04-27 [1]
Biostrings * 2.56.0 2020-04-27 [1]
bit 4.0.4 2020-08-04 [1]
bit64 4.0.5 2020-08-30 [1]
bitops 1.0-6 2013-08-17 [1]
blob 1.2.1 2020-01-20 [1]
broom 0.7.4 2021-01-29 [1]
cachem 1.0.3 2021-02-04 [1]
cellranger 1.1.0 2016-07-27 [1]
cli 2.3.0 2021-01-31 [1]
clipr 0.7.1 2020-10-08 [1]
cluster 2.1.0 2019-06-19 [1]
codetools 0.2-18 2020-11-04 [1]
colorspace 2.0-0 2020-11-11 [1]
crayon 1.4.1 2021-02-08 [1]
data.table 1.13.6 2020-12-30 [1]
DBI 1.1.1 2021-01-15 [1]
dbplyr 2.1.0 2021-02-03 [1]
DelayedArray * 0.14.1 2020-07-14 [1]
desc 1.2.0 2018-05-01 [1]
DESeq2 * 1.28.1 2020-05-12 [1]
details 0.2.1 2020-01-12 [1]
digest 0.6.27 2020-10-24 [1]
dplyr * 1.0.4 2021-02-02 [1]
ellipsis 0.3.1 2020-05-15 [1]
evaluate 0.14 2019-05-28 [1]
extrafont * 0.17 2014-12-08 [1]
extrafontdb 1.0 2012-06-11 [1]
farver 2.0.3 2020-01-16 [1]
fastmap 1.1.0 2021-01-25 [1]
forcats * 0.5.1 2021-01-27 [1]
foreach 1.5.1 2020-10-15 [1]
fs 1.5.0 2020-07-31 [1]
gdtools * 0.2.3 2021-01-06 [1]
genefilter 1.70.0 2020-04-27 [1]
geneplotter 1.66.0 2020-04-27 [1]
generics 0.1.0 2020-10-31 [1]
GenomeInfoDb * 1.24.2 2020-06-15 [1]
GenomeInfoDbData 1.2.3 2020-08-13 [1]
GenomicRanges * 1.40.0 2020-04-27 [1]
ggplot2 * 3.3.3 2020-12-30 [1]
ggpomological * 0.1.2 2020-08-13 [1]
ggrepel * 0.9.1 2021-01-15 [1]
ggsci * 2.9 2018-05-14 [1]
ggtext * 0.1.1 2020-12-17 [1]
ggtree * 2.2.4 2020-07-28 [1]
glue * 1.4.2 2020-08-27 [1]
gridExtra 2.3 2017-09-09 [1]
gridtext 0.1.4 2020-12-10 [1]
gtable 0.3.0 2019-03-25 [1]
haven 2.3.1 2020-06-01 [1]
highr 0.8 2019-03-20 [1]
hms 1.0.0 2021-01-13 [1]
htmltools 0.5.1.1 2021-01-22 [1]
HTSSIP * 1.4.1 2021-01-15 [1]
httr 1.4.2 2020-07-20 [1]
igraph 1.2.6 2020-10-06 [1]
invgamma 1.1 2017-05-07 [1]
IRanges * 2.22.2 2020-05-21 [1]
irlba 2.3.3 2019-02-05 [1]
iterators 1.0.13 2020-10-15 [1]
jsonlite 1.7.2 2020-12-09 [1]
kableExtra * 1.3.1 2020-10-22 [1]
knitr 1.31 2021-01-27 [1]
labeling 0.4.2 2020-10-20 [1]
lattice * 0.20-41 2020-04-02 [1]
lazyeval 0.2.2 2019-03-15 [1]
lifecycle 0.2.0 2020-03-06 [1]
locfit 1.5-9.4 2020-03-25 [1]
lubridate 1.7.9.2 2020-11-13 [1]
magrittr * 2.0.1 2020-11-17 [1]
markdown 1.1 2019-08-07 [1]
MASS 7.3-53 2020-09-09 [1]
Matrix 1.3-2 2021-01-06 [1]
matrixStats * 0.58.0 2021-01-29 [1]
memoise 2.0.0 2021-01-26 [1]
mgcv 1.8-33 2020-08-27 [1]
mixsqp 0.3-43 2020-05-14 [1]
modelr 0.1.8 2020-05-19 [1]
multtest 2.44.0 2020-04-27 [1]
munsell 0.5.0 2018-06-12 [1]
nlme 3.1-152 2021-02-04 [1]
patchwork * 1.1.1 2020-12-17 [1]
permute * 0.9-5 2019-03-12 [1]
phyloseq * 1.32.0 2020-04-27 [1]
pillar 1.4.7 2020-11-20 [1]
pkgconfig 2.0.3 2019-09-22 [1]
plyr 1.8.6 2020-03-03 [1]
png 0.1-7 2013-12-03 [1]
prettyunits 1.1.1 2020-01-24 [1]
progress 1.2.2 2019-05-16 [1]
purrr * 0.3.4 2020-04-17 [1]
R6 2.5.0 2020-10-28 [1]
ragg * 0.4.1 2021-01-11 [1]
RColorBrewer * 1.1-2 2014-12-07 [1]
Rcpp 1.0.6 2021-01-15 [1]
RCurl 1.98-1.2 2020-04-18 [1]
readr * 1.4.0 2020-10-05 [1]
readxl 1.3.1 2019-03-13 [1]
reprex 1.0.0 2021-01-27 [1]
reshape2 1.4.4 2020-04-09 [1]
rhdf5 2.32.4 2020-10-05 [1]
Rhdf5lib 1.10.1 2020-07-09 [1]
rlang 0.4.10 2020-12-30 [1]
rmarkdown 2.6 2020-12-14 [1]
rprojroot 2.0.2 2020-11-15 [1]
RSQLite 2.2.3 2021-01-24 [1]
rstudioapi 0.13 2020-11-12 [1]
Rttf2pt1 1.3.8 2020-01-10 [1]
rvcheck 0.1.8 2020-03-01 [1]
rvest 0.3.6 2020-07-25 [1]
S4Vectors * 0.26.1 2020-05-16 [1]
scales * 1.1.1 2020-05-11 [1]
sessioninfo 1.1.1 2018-11-05 [1]
speedyseq * 0.5.3.9001 2020-10-27 [1]
SQUAREM 2021.1 2021-01-13 [1]
stringi 1.5.3 2020-09-09 [1]
stringr * 1.4.0 2019-02-10 [1]
SummarizedExperiment * 1.18.2 2020-07-09 [1]
survival 3.2-7 2020-09-28 [1]
svglite * 1.2.3.2 2020-07-07 [1]
systemfonts 1.0.1 2021-02-09 [1]
textshaping 0.2.1 2020-11-13 [1]
tibble * 3.0.6 2021-01-29 [1]
tidyr * 1.1.2 2020-08-27 [1]
tidyselect 1.1.0 2020-05-11 [1]
tidytree 0.3.3 2020-04-02 [1]
tidyverse * 1.3.0 2019-11-21 [1]
treeio 1.12.0 2020-04-27 [1]
truncnorm 1.0-8 2018-02-27 [1]
vctrs 0.3.6 2020-12-17 [1]
vegan * 2.5-7 2020-11-28 [1]
viridis * 0.5.1 2018-03-29 [1]
viridisLite * 0.3.0 2018-02-01 [1]
visdat * 0.6.0.9000 2021-02-01 [1]
webshot 0.5.2 2019-11-22 [1]
withr 2.4.1 2021-01-26 [1]
xfun 0.20 2021-01-06 [1]
XML 3.99-0.5 2020-07-23 [1]
xml2 1.3.2 2020-04-23 [1]
xtable 1.8-4 2019-04-21 [1]
XVector * 0.28.0 2020-04-27 [1]
yaml 2.2.1 2020-02-01 [1]
zlibbioc 1.34.0 2020-04-27 [1]
source
CRAN (R 4.0.2)
Bioconductor
Bioconductor
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
Bioconductor
Bioconductor
CRAN (R 4.0.2)
Bioconductor
Bioconductor
Bioconductor
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
Bioconductor
CRAN (R 4.0.2)
Bioconductor
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
Bioconductor
Bioconductor
CRAN (R 4.0.2)
Bioconductor
Bioconductor
Bioconductor
CRAN (R 4.0.2)
Github (gadenbuie/ggpomological@69f3815)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
Bioconductor
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
Github (buckleylab/HTSSIP@29ec56b)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
Bioconductor
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
Bioconductor
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
Bioconductor
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
Bioconductor
Bioconductor
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
Bioconductor
CRAN (R 4.0.2)
CRAN (R 4.0.2)
Github (mikemc/speedyseq@8daed32)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
Bioconductor
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
Bioconductor
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
Github (ropensci/visdat@8121dfe)
CRAN (R 4.0.2)
CRAN (R 4.0.3)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
CRAN (R 4.0.2)
Bioconductor
CRAN (R 4.0.2)
Bioconductor
[1] /home/angel/R/library
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/libraryReferences
Angel R, Panhölzl C, Gabriel R et al. Application of stable-isotope labelling techniques for the detection of active diazotrophs. Environ Microbiol 2018;20:44–61.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550.
Stephens M. False discovery rates: a new deal. Biostatistics 2016;18:275–94.
Youngblut ND, Barnett SE, Buckley DH. HTSSIP: An R package for analysis of high throughput sequencing data from nucleic acid stable isotope probing (SIP) experiments. PLOS ONE 2018;13:e0189616.